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Various degrees of residential segregation by income and race generally
exist in U.S. cities. This study extends Sethi and Somanathan’s theoretical model (J
Polit Econ 112:1296–1321, 2004) by presenting an agent-based sorting, repeatedgame
model to quantify the patterns of segregation from a broader perspective.
Based on the belief that residential racial segregation is a probabilistic problem
without assured results, a numerical model—calibrated to U.S. household income
data—is proposed to examine residential segregation by income and racial preferences.
Similar to the SimSeg model developed by Fosset (J Math Sociol 30:185–274,
2006a; J Math Sociol 35:114–145, 2011), the numerical model we construct is
based on a simple format which also explores segregation dynamics. The simulation
results exhibit various degrees of segregation probability in a hypothetical
three-neighborhood scenario. It also reveals that although income plays an important
role, racial consciousness—the measurement of an agent’s attitude toward the racial
composition of the neighborhood—is the dominant factor in determining residential
segregation.